多机器人协同穿越迷宫，无需全局通信也能高效完成任务。
Design for One, Deploy for Many: Navigating Tree Mazes with Multiple Agents
- 采用动态换头机制，单个机器人解法可直接复用于多机群体。
- 300个机器人实验中，完成时间比基础策略快，燃料消耗接近最优。
- 适合通信受限的复杂环境，如管道、洞穴等分布式场景应用。
迷宫类环境（如洞穴、管道网络）对多机器人协作带来通信限制与拥堵挑战。为此，我们提出一种基于无环图表示的分布式多机器人迷宫遍历算法。该算法采用动态换头机制：一个代理担任领头者，使用任意单机器人迷宫求解器，其余代理各自选择跟随一个代理；当必要时，领头角色会转移给邻近代理，确保整体路径与单机器人解法一致。算法在最多300个代理、多种迷宫规模及多个单机器人求解器的仿真中进行评估，并与盲目策略、需全局通信或完全环境知识的策略对比。结果表明，其在完成时间（makespan）上优于盲目策略，在完成时间上优于全局通信策略，但在燃料总和（sum-of-fuel）上逊于后者。分析显示，其渐近性能与完全已知环境策略相当。真实世界实验中，20台Pi-puck机器人验证了方法的可行性。
原文摘要 · Abstract (English)
Maze-like environments, such as cave and pipe networks, pose unique challenges for multiple robots to coordinate, including communication constraints and congestion. To address these challenges, we propose a distributed multi-agent maze traversal algorithm for environments that can be represented by acyclic graphs. It uses a leader-switching mechanism where one agent, assuming a head role, employs any single-agent maze solver while the other agents each choose an agent to follow. The head role gets transferred to neighboring agents where necessary, ensuring it follows the same path as a single agent would. The multi-agent maze traversal algorithm is evaluated in simulations with groups of up to 300 agents, various maze sizes, and multiple single-agent maze solvers. It is compared against strategies that are naïve, or assume either global communication or full knowledge of the environment. The algorithm outperforms the naïve strategy in terms of makespan and sum-of-fuel. It is superior to the global-communication strategy in terms of makespan but is inferior to it in terms of sum-of-fuel. The findings suggest it is asymptotically equivalent to the full-knowledge strategy with respect to either metric. Moreover, real-world experiments with up to 20 Pi-puck robots confirm the feasibility of the approach.
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